The present invention relates generally to the field of image registration, and more particularly to medical image registration for images showing tumors (and/or other types of lesions (see definition of “lesion,” below, in the Definitions section)).
Image registration refers to the transformation of different sets of image data into a common coordinate system. This image data may take the form of multiple medical images, such as photographs, x-rays or CAT scans. Registration facilitates comparison of multiple images and/or integration of medical data derived from the multiple images. Currently conventional image registration, or image alignment, algorithms can be classified into two types as follows: (i) intensity-based; and (ii) feature-based. In some currently conventional image registration applications, one of the images is referred to as the reference, or source, and the other image(s) are referred to as the target, sensed or subject images. Image registration involves spatially transforming the source/reference image(s) to align with the target image(s). The reference frame in the target image is stationary, while the other datasets corresponding to the source image(s) are transformed to match the co-ordinate system (or, spatial frame of reference) of the target image(s).
Intensity-based methods compare intensity patterns in images using correlation metrics. Feature-based methods find correspondence between image features such as points, lines, and contours. Intensity-based methods register entire images or sub-images. If sub-images are registered, centers of corresponding sub images are treated as corresponding feature points. Feature-based methods establish a correspondence between a number of especially distinct points in images. A correspondence between a number of points in source and target image(s) is determined. A geometrical transformation is then determined based on the point-to-point correspondence. The geometrical transformation is used to map the target image to the reference images, thereby establishing point-by-point correspondence between the reference and target images.
U.S. Patent Application Publication US 2014/0233826 (“Agaian”) states as follows: “Embodiments herein relate to automated quantitative analysis and assessment of human or animal tissue images such as liver biopsy, endometrial biopsy, lung biopsy, lymph node biopsy, renal biopsy, bladder biopsy, rectal biopsy, skin biopsy, and other serial sections/slides of prostate core images. More particularly, but not exclusively, the invention relates to detection, grading, prediction, and staging of prostate cancer on serial sections/slides of prostate core images, or part of biopsy images, which are illustrated as examples . . . . Image edge detection is one of the most effective preprocessing tools that provide essential image edge information and characteristics. An edge detector can be defined as a mathematical operator that responds to the spatial change and discontinuities in a gray-level (luminance) of a pixel set in an image. This method can be used in wide areas such as image segmentation, image categorization, image registration, image visualization, and pattern recognition. These applications may vary in their outputs but they all share the common need of precise edge information in order to carry out the needed tasks successfully.”